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Decision Support for Cargo Pickup and Delivery Under Uncertainty: A Combined Agent-Based Simulation and Optimization Approach

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Publicado en:Computers vol. 14, no. 11 (2025), p. 462-492
Autor principal: Moreno Renan Paula Ramos
Otros Autores: Lopes, Rui Borges, Ramos, Ana Luísa, Vasconcelos Ferreira José, Correia Diogo, de Melo Igor Eduardo Santos
Publicado:
MDPI AG
Materias:
Simulation
Decision support systems
Linear programming
Integer programming
Emissions
Planning
Genetic algorithms
Optimization
Decision making
Stochastic models
Electronic commerce
Literature reviews
Mixed integer
Robustness (mathematics)
Logistics
Baggage handling
Uncertainty
Efficiency
Optimization models
Vehicles
Acceso en línea:Citation/Abstract
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Resumen:This article introduces an innovative hybrid methodology that integrates deterministic Mixed-Integer Linear Programming optimization with stochastic Agent-Based Simulation to address the PDP-TW. The approach is applied to real-world operational data from a luggage-handling company in Lisbon, covering 158 service requests from January 2025. The MILP model generates optimal routing and task allocation plans, which are subsequently stress-tested under realistic uncertainties, such as variability in travel and service times, using ABS implemented in AnyLogic. The framework is iterative: violations of temporal or capacity constraints identified during the simulation are fed back into the optimization model, enabling successive adjustments until robust and feasible solutions are achieved for real-world scenarios. Additionally, the study incorporates transshipment scenarios, evaluating the impact of using warehouses as temporary hubs for order redistribution. Results include a comparative analysis between deterministic and stochastic models regarding operational efficiency, time window adherence, reduction in travel distances, and potential decreases in CO2 emissions. This work provides a contribution to the literature by proposing a practical and robust decision-support framework aligned with contemporary demands for sustainability and efficiency in urban logistics, overcoming the limitations of purely deterministic approaches by explicitly reflecting real-world uncertainties.
ISSN:2073-431X
DOI:10.3390/computers14110462
Fuente:Advanced Technologies & Aerospace Database